Determining images of interest based on a geographical location

ABSTRACT

Methods, systems, and devices are described for identifying images which may be of interest to a user based on their current geographic location. In some embodiments, a check is first performed to determine if the current geographic location is a location-of-interest. Images are searched that are in geographical proximity to the current geographic location of the user to identify images-of-interest. The images-of-interest may be designated in part based on actions taken by subjects having had interactions with the images. The user is notified based on the discovery of one or more images-of-interest. The one or more images-of-interest may be presented to the user through the use of map overlays and/or augmented reality techniques.

RELATED CASES

This application claims the benefit of U.S. Provisional PatentApplication No. 62/621,858 filed on Jan. 25, 2018 and U.S. ProvisionalPatent Application No. 62/666,953 filed on May 4, 2018, the disclosuresof which are incorporated herein by reference in their entirety.

TECHNICAL FIELD

The present disclosure relates to identifying images-of-interest andviewing the images-of-interest using an augmented reality environment.

BACKGROUND

Many millions of images are captured every day, predominately by cellphones. Today's cell phones include a number of sophisticated sensors,and much of the data collected by those sensors may be stored with thecaptured images. Metadata stored with the captured images may includethe geographical capture location, direction of camera, focal distance,etc. Advances in facial recognition have made it easier than ever beforeto accurately tag large numbers of images with the persons appearing inthe images. At the same time, advances in augmented reality (AR) and 3Dvisualization have made it possible to put together compelling mobileuser experiences employing these technologies, as evidenced by therecent popularity of applications like Pokémon Go®. Untappedopportunities exist for marrying the advantages of sensor rich cellphones, images incorporating rich metadata, and navigationalpossibilities of AR and 3D.

SUMMARY OF THE DISCLOSURE

The present disclosure details a system whereby a mobile device user maybe alerted when they arrive at a new geographical area where images havebeen captured in geographical proximity in the past including faces ofsubjects (people) that may be known or of interest. Subject faces ofinterest may be determined by examining the social graph of the userand/or examining the images stored on the cell phone of the user. Oncean image has been designated, a notification may be provided to the cellphone user. Upon selecting the notification, the user may be presentedwith a three-dimensional map showing the current location of the userand the location of the designated image. As the user moves around andchanges geographical position, the map updates, and the perspective onthe captured image us updated.

A system is described wherein a new geographical location is received asinput at the mobile device, typically in GPS coordinate form. Based onthese input coordinates, an interest area surrounding the point isdetermined. The area may take the form of a circle, square, indeed anyarbitrary shape. The interest area is then examined to determine ifimages exist that would be relevant to the user of the mobile device. Insome embodiments, relevant images may be those images containingoverlapping subject faces with the images stored on the mobile device.In some embodiments, relevant images may be those images includingsocial networking friends of the user of the mobile device. If relevantimages are identified, the process continues, otherwise it aborts. Next,exclusion areas are retrieved, and the interest area is examined todetermine if there is overlap between the interest area and at least oneof the exclusion areas. If overlap is found then the process aborts,otherwise the process continues. Next, exclusion scenarios areretrieved, and the interest area is examined to determine if there isoverlap between the current environment and at least one of theexclusion scenarios. If overlap is found then the process aborts,otherwise the process continues. Exclusion scenarios may take the formof a time window. For example, don't provide notification from 11 PM to6 AM. Exclusion scenarios may take the form of an activity, for example,don't provide notification when the user is talking on the phone and/ordriving. Exclusion scenarios may take the form of locations. Don't sendme notification while at work.

It is a goal of the present disclosure to improve the technologyemployed by the various components and devices comprising the system,thereby improving the functionality of the composite system. Theseimprovements in technology are expected to improve the functionalityavailable to users of the system as well, indeed it is a primarymotivation behind improving the technology. Improvements to thecomponents and devices comprising the system and improvements infunctionality available to users should not be considered mutuallyexclusive.

A system of one or more computers can be configured to performparticular operations or actions by virtue of having software, firmware,hardware, or a combination of them installed on the system that inoperation causes or cause the system to perform the actions. One or morecomputer programs can be configured to perform particular operations oractions by virtue of including instructions that, when executed by dataprocessing apparatus, cause the apparatus to perform the actions. Onegeneral aspect includes a server device comprising a communicationsinterface operable to: couple the server device to a plurality of mobiledevices, the plurality of mobile devices including a first mobile deviceassociated with a first user account of a first user and a plurality ofother mobile devices associated with other user accounts of a pluralityof other users; and a processor and memory coupled to the communicationsinterface and operable to: identify a current geographic location of thefirst mobile device of the plurality of mobile devices as alocation-of-interest; identify an image in geographical proximity to thelocation-of-interest as an image-of-interest based on a first userprofile of the first user account; and send, to the first mobile deviceof the plurality of mobile devices, first information identifying theimage-of-interest. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Theserver device may be further operable to: identify the image in thegeographical proximity to the location-of-interest as theimage-of-interest in response to identifying the current geographiclocation of the first mobile device of the plurality of mobile devicesas the location-of-interest; and send, to the first mobile device of theplurality of mobile devices, the first information identifying theimage-of-interest in response to identifying the image in thegeographical proximity to the location-of-interest as theimage-of-interest. The server device may be further operable to:retrieve a plurality of images from a first image collection linked tothe first user account; retrieve social graph information for the firstuser account; and analyze the plurality of images from the first imagecollection and the social graph information to determine subjectaffinities between the first user of the first mobile device and otherusers of other mobile devices. The server device may be further operableto: analyze symmetric social network connections between the first userand ones of other users; analyze asymmetric social network connectionsbetween the first user and ones of other users; analyze occurrences ofsubject faces of the plurality of other users appearing in images in afirst user's collection; and analyze co-occurrences of a subject face ofthe first user of the first mobile device and other subject faces of theplurality of other users of the other mobile devices occurring in thefirst user's collection. The server device may be further operable to:collect geographic location data for the first mobile device on aperiodic basis; store the geographic location data in a trackingdatabase; and determine a home location using the geographic locationdata. The server device may be further operable to: determine a locationaffinity score; and based on the location affinity score, determine ifthe current geographic location is the location-of-interest. The serverdevice may be further operable to: determine a distance between thecurrent geographical location and a home geographical location;determine a number of days since the current geographical location wasvisited; determine a total number of times that the current geographicallocation was visited; and determine the location affinity score based onthe distance between the current geographical location and the homegeographical location, the number of days since the current geographicallocation was visited, and the total number of times that the currentgeographical location was visited. The server device may be furtheroperable to: compute the location affinity score to be proportional tothe distance between the current geographical location and the homegeographical location a last time that the current geographical locationwas visited and inversely proportional to the total number of times thatthe current geographical location was visited. The server device may befurther operable to: modify the location affinity score based on anexclusion bias applied to the current location, wherein the exclusionbias is determined from taking the sum of one or more geographic shapesin which the current location falls, each shape specified on behalf of auser based on the user's profile; and modify the location affinity scorebased on a promotional bias applied to the current location, wherein thepromotional bias is determined from taking the sum of one or moregeographic shapes in which the current location falls, each geographicshape specified by an advertiser and assigned a value based on theremuneration received from an advertiser. The server device may befurther operable to: perform a comparison of the location affinity scoreto a location threshold value; and based on the comparison, determinethat the image is the image-of-interest. The server device may befurther operable to: designate a search area based on thelocation-of-interest; and identify the image as having been capturedwithin the search area surrounding the location-of-interest as theimage-of-interest. The size of the search area may be designated basedon a speed of travel of the first mobile device as measured over a timeinterval. The shape of the search area may be designated based on adirection of travel of the first mobile device as measured over a timeinterval. The size of the search area may be designated based on analtitude of travel of the first mobile device as measured over a timeinterval. The server device may be further operable to: determine animage affinity score; and based on the image affinity score, determineif the image is the image-of-interest. The server device may be furtheroperable to: determine subjects associated with the image byidentifying: a first other user having captured the image; one or moresecond other users appearing as subject faces in the image; and one ormore third other users contributing comments to the image; and determineaction weights by: designating a first action weight for the one or moresecond other users appearing as the subject faces in the image;designating a second action weight for the one or more third other userscontributing comments to the image; and designating a third actionweight for the one or more third other users contributing comments tothe image; and compute the image affinity score by: determining a firstpartial score based on a subject affinity of the first other user andthe first action weight; determining a second partial score based onsubject affinities of the one or more second other users and the secondaction weight; and determining a third partial score based on subjectaffinities of the one or more third other users and the third actionweight; and determining the image affinity score based on the firstpartial score, the second partial score, and the third partial score.The server device may be further operable to: scale the subjectaffinities of the one or more second other users appearing as subjectfaces in the image based on a size of a subject face in relation todimensions of the image and a location of the subject face within theimage. The server device may be further operable to: determine adistance between the current geographical location and a capturelocation of the image; and modify the image affinity score based on thedistance. The server device may be further operable to: modify the imageaffinity score based on an exclusion bias applied to the capturelocation of the image, wherein the exclusion bias is determined fromtaking the sum of one or more geographic shapes in which the capturelocation of the image falls, each shape specified on behalf of a userbased on the user's profile; and modify the image affinity score basedon a promotional bias applied to the capture location of the image,wherein the promotional bias is determined from taking the sum of one ormore geographic shapes in which the capture location of the image falls,each geographic shape specified by an advertiser and assigned a valuebased on the remuneration received from an advertiser. The server devicemay be further operable to: compute the image affinity score for theimage to be: proportional to the subject affinity score; and inverselyproportional to a distance between current a geographical location and acapture location. The server device may be further operable to: performa comparison of the image affinity score to an image threshold value;and based on the comparison, determine that the image is theimage-of-interest. The server device may be further operable to:receive, from the first mobile device of the plurality of mobiledevices, second information identifying user interactions with theimage-of-interest at the first mobile device; and adjust the parameterweights of subject affinity sources and subject actions based on thesecond information identifying the user interactions. The server devicemay be further operable to: send promotional information associated withthe image to the first mobile device, the promotional informationincluding one or more adornments configured for presentation with theimage. The server device may be further operable to: receive, from thefirst mobile device, user interaction feedback, the user interactionfeedback including: time difference between user receive a notificationof the image, time spent by a user interacting with the image, commentsadded, likes applied, and dislikes applied. The server device may befurther operable to: receive, from the plurality of other mobile devicesassociated with other user accounts of the plurality of other users, aplurality of other images; and identify, from the plurality of otherimages, the image-of-interest. The image-of-interest may be one of oneor more images-of-interest and the location-of-interest may be one ofone or more locations-of-interest. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

In another general aspect a method of operating a server device isdisclosed comprising: identifying an image, in geographical proximity toa current geographic location of a mobile device of a plurality ofmobile devices, as an image-of-interest based on a user profile of auser account of a user of the mobile device; and sending, to the mobiledevice of the plurality of mobile devices, information identifying theimage-of-interest. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Theimage may be identified without determining that the current geographiclocation is a location-of-interest. The image may be identified afterdetermining that the current geographic location is alocation-of-interest.

In another general aspect a method of operating a server device isdisclosed comprising: receiving a target geographic location from adevice; identifying an image in geographical proximity to the targetgeographic location as an image-of-interest; and sending, to the device,information identifying the image-of-interest. Other embodiments of thisaspect include corresponding computer systems, apparatus, and computerprograms recorded on one or more computer storage devices, eachconfigured to perform the actions of the methods.

Implementations may include one or more of the following features. Theimage may be identified without determining that the target geographiclocation is a location-of-interest. The target geographic location maybe one of a current geographic location the and not the currentgeographic location of the device. The device may be one of a mobiledevice and a non-mobile device. The server device may further comprise:receiving, from the device, a distance value defining the geographicalproximity. The distance value defining the geographical proximity may bedetermined based on bounds of a map to be shown on the device.

In another general aspect a method of operating a server device isdisclosed comprising: receiving a target geographic area from a device;identifying an image within the target geographic area as animage-of-interest; and sending, to the device, information identifyingthe image-of-interest. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Theimage may be identified without determining that the target geographicarea includes a location-of-interest. The target geographic area may beone of including a current geographic location and not including thecurrent geographic location of the device. The device may be one of amobile device and a non-mobile device. The target geographic area may bedetermined based on bounds of a map to be shown on the device.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

The accompanying drawing figures incorporated in and forming a part ofthis specification illustrate several aspects of the disclosure, andtogether with the description serve to explain the principles of thedisclosure.

FIG. 1A is a system diagram of the entities comprising the presentdisclosure;

FIG. 1B illustrates an exemplary data structure for storing a useraccount repository according to some embodiments;

FIG. 1C illustrates an exemplary data structure for storing an imagerepository according to some embodiments;

FIG. 1D illustrates an exemplary data structure for storing a maprepository according to some embodiments;

FIG. 1E illustrates an exemplary data structure for a communicationsqueue according to some embodiments;

FIG. 1F illustrates an exemplary data structure for a tracking datarepository according to some embodiments;

FIG. 1G illustrates an exemplary data structure for a promotionsrepository according to some embodiments;

FIG. 2A illustrates an exemplary illustration of an image beingpresented in an augmented reality environment from a first visualperspective;

FIG. 2B illustrates an exemplary illustration of an image beingpresented in an augmented reality environment from a first visualperspective;

FIG. 3A is a network diagram showing the communications between mobiledevices and the server device;

FIG. 3B is a network diagram showing the communications between mobiledevices and the server device;

FIG. 4 is a flowchart illustrating the process involved in determiningif a location is a location-of-interest, determining images-of-interest,presenting them, collecting feedback, and adjusting computationalparameters;

FIG. 5 is a flowchart illustrating an exemplary process for initializingand updating a user profile;

FIG. 6 is a flowchart illustrating an exemplary process for determiningif a location is a location-of-interest;

FIG. 7 is a flowchart illustrating an exemplary process for determiningone or more images-of-interest;

FIG. 8A is a flowchart illustrating an exemplary process for determininga location-of-interest;

FIG. 8B is a flowchart illustrating an exemplary process for determiningan image-of-interest;

FIG. 9 is a flowchart illustrating an exemplary process for presentingone or more images-of-interest;

FIG. 10 is a flowchart illustrating an exemplary process for collectingfeedback and adjusting user preferences;

FIG. 11A graphically illustrates an exemplary user interface for settingpreferences on a mobile device;

FIG. 11B graphically illustrates an exemplary user interface forspecifying subject face preferences on a mobile device;

FIG. 11C graphically illustrates an exemplary user interface forspecifying geographical location preferences on a mobile device;

FIG. 11D graphically illustrates an exemplary user interface forspecifying time period and preferences on a mobile device;

FIG. 11E graphically illustrates an exemplary user interface specifyingnotification preferences on a mobile device;

FIG. 11F graphically illustrates an exemplary user interface specifyingnotification preferences on a mobile device;

FIG. 11G graphically illustrates an exemplary user interface for anotification dialog on a mobile device;

FIG. 11H illustrates an exemplary user interface for presenting animages-of-interest map on a mobile device;

FIG. 12A graphically illustrates a map showing the locations used inFIG. 12B that are tested to determine if they are locations-of-interest;

FIG. 12B graphically illustrates an exemplary computation fordetermining a geographical location-of-interest;

FIG. 13A graphically illustrates an exemplary image;

FIG. 13B graphically illustrates the image in an exemplary page from asocial network showing activities related to an image that are used incomputing subject affinity;

FIG. 13C graphically illustrates an exemplary computation fordetermining an image affinity score;

FIG. 13D graphically illustrates an exemplary computation fordetermining images-of-interest;

FIG. 13E graphically illustrates locations of images-of-interestoverlaid on a map;

FIG. 13F graphically illustrates the images-of-interest overlaid on themap of FIG. 13E;

FIG. 13G graphically illustrates a promotional bias overlaid on the mapof FIG. 13E;

FIG. 14 illustrates the images, and element found therein, for theexample shown in FIGS. 13A-13E;

FIG. 15 is an exemplary hardware architecture block diagram for anexemplary machine;

FIG. 16 is an exemplary software architecture block diagram for themachine of FIG. 15;

FIG. 17 graphically illustrates a block diagram of an exemplary mobiledevice; and

FIG. 18 graphically illustrates a block diagram of an exemplary serverdevice.

DETAILED DESCRIPTION

FIG. 1A illustrates an exemplary view of a system diagram for someembodiments of the present disclosure. The system includes a pluralityof mobile devices 20 [1-N] coupled to a server device 60. The mobiledevice 20 includes a control system 21. The control system 21 includes aweb module 22, client module 23, configuration module 24, triggeringmodule 26, notification module 28, affinity prediction module 30, userinterface module 32 and imaging module 40.

The configuration module 24 operates to initialize the mobile device 20for operation. The triggering module 26 determines when one or moreimages of interest have been encountered and triggers the notificationmodule 28 to queue a notification for present to the user 12 of themobile device 20. The affinity prediction module 30 operates todetermine an affinity score for one or more images, which is provided tothe triggering module 26.

The user interface (UI) module 32 operates to render map data on thedisplay of the mobile device 20 and includes a 3D engine 34, augmentedreality renderer 36, and a navigation module 38. The 3D Engine 34renders map data to produce a three-dimensional visualization. Theaugmented reality renderer 36 presents the one or more images ofinterest in the three-dimensional space. Based on user 12 inputs, thenavigation module 38 moves the view perspective around within thethree-dimensional space.

The imaging module 40 includes a facial detection module 42, facialmatching module 44, and facial identification module 46 and performsoperations on the images. The facial detection module 42 determines theexistence and position of faces within an image. The facial matchingmodule 44 operates to determine whether a face appearing in twodifferent images is the same. The facial identification module 46operates to determine the identity of a face appearing in an image.

The server device 60 includes a control system 61. The control system 61includes a web module 62 and a client module 63 which includes anaccount module 64, imaging module 66, map module 68, and a communicationmodule 70.

The web module 64 operates to present web content and facilitateinteractions with the user in regards to said web content. The accountmodule 65 operates to create, modify, store, and more generally manageinformation related to user accounts 201 in the user account repository200. The imaging engine 66 operates to create, modify, store, and moregenerally manage information related to images 231 in the imagerepository 230. The map module 68 operates to create, modify, store, andmore generally manage information related to maps 251 in the maprepository 250. The communications module 70 operates to create, modify,store, and more generally manage information related to communicationsmodule 70 in the communications queue repository 260. The trackingmodule 72 operates to create, modify, store, and more generally manageinformation related to tracking items 271 in the tracking datarepository 270. The promotion module 74 operates to create, modify,store, and more generally manage information related to promotion items281 in the promotion repository 280.

The repositories 80, including the account repository 200, imagerepository 230, map repository 250, communications queue repository 260,tracking data repository 270, and promotions repository 280, may bestored in a filesystem at the server device 60, a database, networkattached storage, storage attached network, blockchain, or anycombination thereof.

The advertiser device 90 is operates to submit promotions to thepromotions repository using the promotions module 74.

The network 15 is preferably a distributed, public access network, suchas the Internet, wherein the server device 60, mobile device 20 andadvertiser device 90 are capable of interacting with and through thenetwork 15 using various protocols such as Transmission ControlProtocol/Internet Protocol (TCP/IP), Hypertext Transport Protocol(HTTP), and File Transfer Protocol (FTP). However, those of ordinaryskill in the art will appreciate that the network 15 is not limitedthereto. More specifically, the network 15 may be any type of networksuitable to allow interaction between the server device 60, mobiledevice 20 and advertiser device 90. For example, the network 15 may be awired network, a wireless network, or any combination thereof. Further,the network 15 may include a distributed computing network, an intranet,a local-area network (LAN) and/or a wide-area network (WAN), or anycombination thereof.

The repositories 71, including the account repository 200, imagerepository 220, venue repository 240, shotspot repository 250, camerarepository 260, and session repository 270, may be stored in afilesystem at the server device 60, a database, network attachedstorage, storage attached network, blockchain, or any combinationthereof.

FIG. 1B illustrates an exemplary data structure 200 for storing the useraccount repository 200 according to some embodiments. The account module64 performs operations on the account repository 200. The accountrepository 200 stores zero or more account records 201. Each accountrecord 201 includes a user identification (ID) 202, credentials 203,location history 204, user profile 205, encounter history 208,acquaintance list 212, and exclusions list 217.

The user ID 202 uniquely identifies the user 12 that is the accountowner within the system. The credentials 203 stores a user name andpassword.

The user profile 205 stores information about the user's preferences.The user profile 205 includes a subject affinity 206 and a locationaffinity 207. The subject affinity 206 identifies the subject affinitybetween the account owner and one or more other subjects. The locationaffinity 207 identifies the location affinity between the account ownerand one or more other geographic locations. These values

The encounter history 208 stores a history of the mobile device 20interaction with images of interest. The encounter history 208 includesthe image that was the subject of the encounter 209, location 210encounter and interactions 211 comprising the encounter. The imageencountered 209 indicates the image that was encountered. The location210 identifies the geographic location at which the image wasencountered. Interactions 211 identifies the interactions the user 12had with the image viewed 209.

The acquaintance list 212 stores a list of acquaintances of the accountowner. The acquaintance list 212 includes an acquaintance ID 213,connection type 214, affinity prediction 215, relationship duration 216.The acquaintance ID 213 stores a unique identifier of the acquaintancewithin the system. The connection type 214 describes the type ofconnection between the account owner and acquaintance. The affinityprediction 215 stores a numerical representation of an affinityprediction between the account owner and acquaintance (subjectaffinity). The relationship duration 216 stores the length of therelationship between the account owner and acquaintance. Examples ofacquaintances include social network friends, contact list contacts,messenger contacts, persons being followed online, etc.

The exclusions list 217 stores a list of geographical exclusion areasfor the account owner. The exclusions list 217 includes exclusion areacoordinates 218, exclusion schedule 219, and exclusion bias 220. Theexclusion area coordinates 218 identify the geographical area of theexclusion. The exclusion schedule 219 identifies the one or moretimes/dates during which the bias should be applied. The exclusion bias220 identifies the magnitude of the exclusion.

FIG. 1C illustrates an exemplary data structure 230 for storing an imagerepository 230 according to some embodiments. The imaging module 66performs operations on the image repository 230. The image repository230 stores zero or more images 231. Each image 231 includes an image ID232, image name 233, face list 234, capture location 235, capturerlocation 236, focal distance 237, view pitch 238, view yaw 239, and viewroll 240. The image ID 232 uniquely identifies the image within thesystem. The image name 233 stores the alpha numeric name of the image.The face list 234 stores a list of the subject faces appearing in theimage. The subject face representations may be produced by conventionalfacial representation generation techniques, such as techniquesdescribed in either or both of U.S. Pat. No. 5,164,992, entitled “Facerecognition system” and U.S. Pat. No. 6,292,575, entitled “Real-timefacial recognition and verification system”. The face representation istypically in the form of a vector, which identifies each of the personsin the subject face list 234. The capture location 235 stores thegeographic location of the device that captured the image at the time ofcapture. The focal location 236 stores the geographic location that wasthe focal point of the image. The focal distance 237 stores the distancebetween the capture location 235 and the focal location 236. The viewpitch 238 stores the vertical viewing angle. The yaw pitch 239 storesthe horizontal viewing angle. The view roll 240 stores the rotationangle along the viewing axis.

FIG. 1D illustrates an exemplary data structure 250 for storing a maprepository 250 according to some embodiments. The map module 68 performsoperations on the map repository 250. The map repository 250 stores zeroor more map segments 251. Each map segment 251 includes a map ID 252,map name 253, and map boundaries 254. The map ID 252 uniquely identifiesthe map segment 251 within the system. The map name 253 stores the alphanumeric name of the map segment 251.

FIG. 1E illustrates an exemplary data structure 260 for a communicationsqueue 260 according to some embodiments. The communications between themobile device 20 and the server 60 are designed to operateasynchronously so that images captured at the mobile device and receivedat the server when there is no cellular or WIFI connection may beexchanged. This is achieved by both the client and the servermaintaining a communications queue on each end. When connectivity isre-established, any pending items in the queues are transmitted. Thecommunications queue 260 is comprised of zero or more communicationsqueue records 261. Each communications queue records 261 includes anitem ID 262, item name 263, sender ID 264, receiver ID 265, creationtimestamp 266, and priority 267. The item ID 262 uniquely identifies thecommunications queue record 261 within the queue. The item name 263stores an alphanumeric description of the communications queue record261 contents. The sender ID 264 uniquely identifies the originator ofthe communications queue record 261 and the receiver id 265 uniquelyidentifies the intended recipient. The creation timestamp 266 representsthe time/date that the communications queue record 261 was created. Thepriority 267 indicates the relative priority of the communications queuerecord 261 within all communications queue records 261.

FIG. 1F illustrates an exemplary data structure 270 for a tracking datarepository 270 according to some embodiments. The tracking datarepository 270 includes zero or more track items 271, each track itemincluding a track id 272, track point location 273, and track timestamp274. The track id 272 uniquely identifies the track item 271 among alltrack items 271 in the tracking data repository 270. The promotion trackpoint location 273 identifies the point location 273 at which the trackitem 271 was recorded. In some embodiments, the track point location 273is specified by a GPS coordinates. The track timestamp 274 identifiesthe date and time at which the track item 271 was recorded.

FIG. 1G illustrates an exemplary data structure 280 for a promotionsrepository 280 according to some embodiments. The promotions repository280 includes zero or more promotion items 281, each promotion itemincluding a promotion id 282, promotion geographical area 283, andpromotion weight 284. The promotion id 282 uniquely identifies thepromotion item 281 among all promotion items 281 in the promotionsrepository 280. The promotion geographical area 283 identifies theboundaries of the geographical area to which the promotion weight 284applies. In some embodiments, the promotion geographical area 283 isspecified by three or more GPS coordinates and may be of arbitraryshape. In some embodiments, the promotion geographical area 283 isspecified by a single GPS coordinate and any information necessary tospecify a shape and its corresponding size. The shape of the promotiongeographical area 283 may be a triangle, square, rectangle, circle, etc.The promotion weight 284 identifies the weight that is to be applied tothe promotion items 281 when ranking the promotion item 281 againstother promotion items 281 and/or computing a location-of-interest score.

FIG. 2A illustrates an exemplary illustration 290 of an image beingpresented in an augmented reality environment from a first visualperspective of a viewer. The image 291 is superimposed on the map 290.In some embodiments, the location 292 of the person having captured theimage is presented on the map 295.

FIG. 2B illustrates an exemplary illustration 295 of an image beingpresented in an augmented reality environment from a second visualperspective of the viewer. The second visual perspective is lower to theground and to the left.

FIG. 3A illustrates an exemplary network diagram 300 for the system ofFIG. 1. Images are captured 302 by the mobile devices 20. The capturedimages are stored with corresponding metadata. The metadata may includecapture time, date, geographic location, camera settings, and the like.The captured images are analyzed 304 to determine subject faces presentin the images using the imaging module 40. In some embodiments metadatais extracted 306 from the images and sent to the server device 60 forstorage. In some embodiments, reduced resolution versions of the imagesare sent 310 to the server device 60 for storage. In some embodiments,full resolution versions of the images are sent 312 to the server device60 for storage. In some embodiments, duplicative image data and/ormetadata is stored at both the mobile device 20 and server device 60.The mobile device 20-1 monitors 314 the geographic location of themobile device 20 and sends 316 the information to the server device 60as the location changes. If the server device 60 determines 318 that themobile device 20 has arrived at a new location, then the server device60 determines 320 if there are new images in the interest area accordingto FIG. 4. If new images are found 320, a notification is sent 322 tothe mobile device 20-1.

FIG. 3B is a network diagram 300 showing the communications between themobile devices and the server device 60. The mobile device 20 providesnotification to the user 12 and based on user 12 input received 324 inresponse to the notification, the mobile device 20 may present anaugmented reality view by presenting a map 328 and overlaying 330 one ormore images on the map. The user 12 of the mobile device 20 provides 332navigational inputs to the mobile device 20 by moving the device aroundin the physical world as the mobile device 20 monitors the pitch, yaw,and roll of the mobile device 20 in real-time. This is monitored 334 atthe mobile device 20 and the augmented reality view is updated 336,using additionally requested 335 and received 336 map data if needed bypresenting 338 the map according to the new updated perspective andpresenting 340 the images according to the updated perspective. As theuser leaves the geographic location, the steps of FIG. 3A are repeated.

FIG. 4 describes an exemplary process 400 for determining one or moreimages-of-interest. A user profile is initialized 402 using elementssuch as those described in FIG. 5. Based on a current geographicallocation, a determination 404 is made as to whether the current locationshould be treated as a location-of-interest. Some embodiments accomplishthis using elements such as those described in FIG. 6. A check isperformed 406, and based on the outcome the new location can either beignored 408, or the process continued 415 determining 412 one moreimages-of interest. Some embodiments accomplish this determination usingelements such as those described in FIG. 7. If no images-of interest areidentified 416, the current location is ignored. Otherwise 418, theprocess continues with the presentation 420 of one or more images-ofinterest. Some embodiments accomplish this presentation using elementssuch as those described in FIG. 9. Interaction feedback is collected 422based on user 12 inputs collected at the mobile device based on thepresentation of the one or more images-of-interest. Based on thecollected interaction feedback, the user profile is updated 424, and theprocess waits 426 for a new current location to be identified. Processflow then repeats 428.

FIG. 5 describes an exemplary process 500 for initializing and/orupdating a user profile. Social Networks of which the user 12 of themobile device 20 is associated are queried 502 to extract informationidentifying symmetric social friends of the user 12 to determine subjectaffinities. The same and/or different/additional social networks arequeried to determine asymmetric social friends. Image collectionsassociated with the user 12 are analyzed to identify subject faceco-occurrence 506 and determine subject affinities. Image collectionsassociated with the user 12 are analyzed to identify subject facesoccurring in those collections 508 and determine subject affinities.Contacts associated with the user 12 are analyzed 510 to determinesubject affinities. A subject affinity matrix is assembled based on theaforementioned collected information 512. Process flow is complete 514.

As used herein, the term “social network” refers to a server device thatenables client devices associated with users to create and storeelectronic friend relationship information. Those friend relationshipsmay be symmetric in that one user invites another user to connect (orlink), and the other user must accept the electronic invitation beforethe symmetric friend relationship is created and stored by the serverdevice. The friend relationships may also be asymmetric in that one usermay request to follow another user, and the other user need not acceptbefore the asymmetric friend relationship is created and stored by theserver device. In some embodiments, the server device may be operable tosupport both symmetric and asymmetric friend relationships. Examples ofserver devices that should not be considered social networks are e-mailsystems and trust networks. With e-mail, all you need is someone e-mailaddress to be able to communicate with them and friending is notrequired. Trust networks typically operate on inference engines wheretrust is inferred by actions taken by the various users who need not beconnected as friends. A server device may be both a social network and atrust network, but just by being one, does not automatically make it theother. An example of a trust network is a news site that enablesvisitors to comment on articles. Visitors that often contribute valuablecomments are awarded a high trust rating. Visitors who contribute offtopic comments laced with profanity are awarded a low trust rating.

As used herein, the term “social graph” refers to the electronic friendconnections (symmetric and/or asymmetric) typically stored by the serverdevice and representing the aforementioned relationships. In someembodiments, this information may be available for export by the serverdevice, such as is the case with Facebook Connect.

As used herein, the term “social distance” refers to the number of hopsin the social graph to get from one user to another user. For example,the social distance between two friends is one. The social distancebetween a user and a friend of a friend of a friend is three. The lowerthe social distance, the closer the relationship.

FIG. 6 describes an exemplary process 600 for determining if a currentlocation is a location-of-interest. A home geographical location isdetermined 602. This may be accomplished by tracking the geographiclocation movement of the mobile device of the user 12 and identifying alocation where the device is stationary for the largest portion of theday. The tracking date of the geographic location movement of the mobiledevice of the user 12 may be stored in a tracking data repository 270.For example, if a user 12 sets their mobile device on the night standfor eight hours a night, that is typically indicative of a homelocation. The home geographical location may be expressed in GPSlatitude/longitude coordinates. A current geographical location may bedetermined 604 in a number of ways. Examples include from the GPSreceiver on the mobile device, WIFI triangulation, cellular towertriangulation, etc. Multiple determination methods may be combined. Thecurrent geographical location may be expressed in GPS latitude/longitudecoordinates. The distance between the home location and current locationis determined 606. A number of processes exist for making thisdetermination, including the haversine formula shown in Equation 1 asshown below. Using the tracking database, the time since a mobile deviceof the user 12 last visited the current location is determined 608 byidentifying the most recent entry in the database from said location andtaking the difference of the current time to the timestamp of thatentry. The total number of visits to the current location is determined610 by identifying the number of entries in the tracking databasecorresponding to the current location. Two locations may be treated asthe same location for purposes of comparison if they differ by less thana difference threshold. Examples of difference thresholds may include 10ft, 100 ft, 1000 ft, a half mile, a mile, 5 miles, 10 miles, 20 miles,etc. For example, the length of the island of Manhattan 1206-34 is 13miles. To determine previous visits to Manhattan 1206-34 when the mobiledevice is located in the center of the island, a difference threshold of13/2 miles may be used. One or more promotional biases that apply to thecurrent location may be retrieved 612 from the promotions repository280. Next, a location affinity score is determined 614. In someembodiments, the score is calculated to be proportional to time sincelast visit, distance from home, and promotional bias and inverselyproportional to total number of visits. In some embodiments, Equation 2as shown below is used to compute the location affinity score. Thelocation affinity score is returned 616.

FIG. 7 describes an exemplary process 700 for determining one or moreimages-of-interest based on a location-of-interest. A search area 1390is designated 702 for bounding the search. Images have been capturedwithin the search area are determined 703. If no images are identified706 for the search area, the process ends 722. If one or more images areidentified 708, scores are determined 710 for the images captured withinthe search area. The one or more images may be sorted 712 by theiraffinity scores. A comparison is made 714, and If all images are above athreshold 716, the process ends. Otherwise 718, the images below thethreshold are removed 720. Zero or more images-of-interest images arereturned 722.

In some embodiments, the size of the search area is designated based ona speed of travel of the first mobile device as measured over a timeinterval. For example, if the user is traveling in a car versus walkingon foot, then the search area may be made larger since the area beingcovered by the mobile device is larger.

In some embodiments, the shape of the search area is designated based ona direction of travel of the first mobile device as measured over a timeinterval. For example, if the user has been traveling in a straight linefor a period of time, it is likely that the direction of travel willremain the same for the immediate future. Thus, the shape of the searcharea may be elongated all the expected travel path.

In some embodiments, the size of the search area is designated based onan altitude of travel of the first mobile device as measured over a timeinterval. For example, if the user is traveling at 30,000 feet in anairplane, then search area may be made larger based on the user'sability to see more of the earth's surface.

FIG. 8A is a flowchart illustrating an exemplary process 800 fordetermining a location-of-interest. Referring now to FIG. 8A, the serverdevice 60 receives 802 a geographical location. Factors are collected804 for determining location affinity. In some embodiments, factors suchas those shown Table 1 shown below are used. A distance between homelocation and geographical location is determined 806. In someembodiments, an equation such as Equation 1 shown below is used. Anintermediate location affinity score (ILAS) is determined 808. In someembodiments, an equation such as Equation 3 shown below is used. Alocation affinity score (las) is determined 810. In some embodiments, anequation such as Equation 4 shown below is used. A final locationaffinity score (FLAS) is determined 812. In some embodiments, anequation such as Equation 5 shown below is used. A comparison betweenthe final location affinity score and a location threshold value (LTV)814 is performed. The result of the comparison is returned 816.

FIG. 8B is a flowchart illustrating an exemplary process 820 fordetermining an image-of-interest. Referring now to FIG. 8B, the serverdevice 60 collects 822 sources for determining subject affinity. In someembodiments, factors such as those shown Table 2 shown below are used.Subject actions for determining subject affinity are determined 824. Insome embodiments, subject actions such as those shown Table 3 shownbelow are used. Subject affinity(s) for one or more subjects associatedwith an image is determined 826. In some embodiments, an equation suchas Equation 6 shown below is used. Intermediate image affinity score(IIAS) is determined 828. In some embodiments, an equation such asEquation 7 shown below is used. Image affinity score (IAS) is determined830. In some embodiments, an equation such as Equation 8 shown below isused. Final image affinity score (FIAS) is determined 832. In someembodiments, an equation such as Equation 9 shown below is used. Acomparison of the final image affinity score (FIAS) to an imagethreshold value (ITV) is performed 834. The result of the comparison isreturned 836.

FIG. 9 is a flowchart illustrating an exemplary process 900 forpresenting one or more images-of-interest. Referring now to FIG. 9, oneor more image(s)-of-interest are received 901 from the server device 60.If the mobile device 20 receiving the one or more image(s)-of-interestis within 902 visual range of the capture location of ones of the one ormore image(s)-of-interest, the images may be presented using augmentedreality techniques. Otherwise 904, a check is performed 910 to determineif map data is available for a 3D presentation. If yes 914, the one ormore image(s)-of-interest are presented 916 using the 3D engine. If not912, the one or more image(s)-of-interest are presented 918 using 2D mapdata. In all cases, user interaction feedback is collected 920 and sentto the server device for storage as encounter history 208. In someembodiments, the user is able to manually select the presentation mode,if available, for presentation of the one or more image(s)-of-interest.

FIG. 10 describes an exemplary process 1000 for collecting feedback andadjusting user preferences. In general, if the user 12 of the mobiledevice 20 was notified of the presence of an image-of-interest and didnot interact with it 1008, the preferences are adjusted to lessen thatprobability that images like that image will be scored as animage-of-interest. If the user 12 of the mobile device 20 was notifiedof the presence of an image-of-interest and did interact with it 1006,the preferences are adjusted to increase the probability that imageslike that image will be scored as an image-of-interest. Referring now tothe flowchart, user interaction feedback is received 1001 from themobile device 20. Image attributes, such as who captured the image,commenters, subject faces within the image, geographical location (GPScoordinates), and capture time are retrieved 1002. A check is made todetermine if the image was interacted with 1004. If the image was notviewed 1008, then weightings of the various parameters are decreasedand/or de-emphasized. In particular, subject affinities for thosesubject faces appearing in the image are decreased 1018 and the locationaffinity associated with the location at which the image was captured isdecreased 1019. If the image was interacted with 1006, the encounter isrecorded 1010. The amount of time spent interacting with the image isdetermined and recorded 1012. Specific interactions with the image suchas viewing, commenting, annotating, forwarding, posting, saving,copying, retweeting, etc., are recorded 1014. Weightings of the variousparameters are increased and/or emphasized 1016. The process completes1020.

FIG. 11A graphically illustrates an exemplary user interface 1100 forsetting preferences on a mobile device. The preference dialog window1100 includes a title 1102 and number of controls for navigating toother sub-dialog windows. The controls include a subject facepreferences button 1103 for navigating to a subject face preferencesdialog window 1110 a geographical locations preferences button 1104 fornavigating to a geographical locations preferences dialog window 1120, atime period preferences button 1105 for navigating to a time periodpreferences dialog window 1130, a notifications preferences button 1116for navigating to a notifications preferences dialog window 1140, apresentation preferences button 1117 for navigating to a presentationpreferences dialog window 1150, and a “done” button 1118 for leaving thepreference dialog window 1100.

FIG. 11B graphically illustrates an exemplary user interface 1110 forspecifying search preferences on a mobile device 20 for identifyinglocations-of-interest. The search preferences dialog window 1110includes a title 1112, subject faces controls 1114, subject faceselection control 1116 and image source controls 1118.

The server device designates images-of-interest in part by determining asubject affinity between the user and other subjects appearing in animage.

The subject faces controls 1114 identify subject faces that may beincluded or excluded using the subject face selection control 1116. Thesubjects faces shown may be derived from faces appearing in images inthe users collection, social network friends and images, contact list,buddy list, or any combination thereof.

The present disclosure describes methods for identifying images that maybe images-of-interest. To accomplish this, the server device 60 searchesone or more images sources to identify the images-of-interest. The imagesources may be specified by source controls 1118. Source control 1118-1enables the searching of a user's 12 collection. The user imagecollection may reside on the mobile device 20, image repository 230, orany combination thereof. Source control 1118-2 enables the searching ofa user's social network image collection. Any number of social networksmay be searched. The server device 60 preferably searches all imagesavailable at the social network that have privacy settings enabling theuser to access said images. Source control 1118-3 enables the searchingof a user's contacts list images. Source control 1118-4 enables thesearching of a user's messenger buddy list. Other sources may besearched. Any number of sources may be searched. The server device 60preferably has access to a large pool of images, some belonging to theuser, some belonging to others. This allows the server device 60 toidentify images that the user has not previously encountered. While notshown, preferred embodiments would include support for listing,searching, sorting, and pagination controls for managing subject facepreferences.

FIG. 11C graphically illustrates an exemplary user interface 1120 forspecifying geographical location preferences on a mobile device 20. Thelocation preferences dialog window 1120 includes a title 1122, a maparea 1123 to display a geographical area, and a location selection shape1122 for specifying a specific location area on the map. The locationpreferences dialog window may be used to specify positive or negativebiases to the area. For example, the location preferences dialog window1120 may be used to specify areas that will affect the weights of theLocation Exclusion Bias (LEB) 1221 values and the image exclusion bias(IEB) 1354 values. A similar user interface may be used by advertisersassociated with advertiser devices 90 to specify geographic areas thatwill control the values for location promotion bias (LPB) 1222 and imagepromotion bias (IPB) 1355. A slider control 1124 operates to enable auser 12 of the mobile device 20 to control the bias assigned to aselected area 1122 by moving the “thumb” 1127 of the control. The valuemay be positive 1126 or negative 1125. While not shown, preferredembodiments would include support for listing, searching, sorting, andpagination controls for managing multiple location preferences.Preferred embodiments would include controls for allowing the user toselect from existing shapes, including a circle (shown), square etc, orfree draw arbitrary closed polygons.

FIG. 11D graphically illustrates an exemplary user interface 1130 forspecifying time period preferences on a mobile device 20. The timeperiod preferences dialog window 1130 includes a title 1142, andcontrols for specifying the time period including a title 1134, starttime 1136, end time 1138, and repeat indicator 1139. A slider control1133 operates to enable a user 12 of the mobile device 20 to control thebias assigned to a selected time period 1132 by moving the “thumb” 1136of the control. The value may be positive 1135 or negative 1134. Notethat the time period preferences are not used in the exemplarycomputations shown in FIGS. 12B and 13D. However, they may be used insome embodiments to further control the biases as described in thedescription of FIG. 11C above. While not shown, preferred embodimentswould include support for listing, searching, sorting, and paginationcontrols for managing multiple time period preferences. In someembodiments, the time period preferences may be linked to the user'scalendar and inferred from existing entries.

FIG. 11E graphically illustrates an exemplary user interface 1140specifying notification preferences on a mobile device. The notificationpreferences dialog window 1140 includes a title 1142 and controls forsetting the preferences. The user 12 of the mobile device 20 may electto receive or block notifications based on criteria including arrivingat a new location of interest 1143, encountering a new image of interest1144, suppressing notifications while driving 1145, suppressingnotification at home 1146, and suppressing notifications at work 1147.Notifications may be provided by the mobile device using one or moremechanism including e-mail, text, native phone notification services,etc.

FIG. 11F graphically illustrates an exemplary user interface 1150specifying notification preferences on a mobile device. The notificationpreferences dialog window 1150 includes a title 1152 and controls foradjusting location-of-interest and image-of-interest thresholds. Aslider control 1153 operates to enable a user 12 of the mobile device 20to control the Location Threshold Value (LTV) 1225 by moving the “thumb”1156 of the control between lower values 1155 and higher values 1152. Aslider control 1157 operates to enable a user 12 of the mobile device 20to control the Image Threshold Value (ITV) 1358 by moving the “thumb”1158 of the control between lower values 1157 and higher values 1156.

FIG. 11G illustrates an exemplary user interface 1160 for a notificationdialog on a mobile device 20. The notification dialog window 1160includes a title 1162 and a display area for presenting notifications. Afirst notification 1164 resulting from the detection of aLocation-of-Interest (LOI) 1226 is presented. In some embodiments, theuser may invoke the first notification to “SEARCH FOR IMAGES”. A secondnotification 1166 resulting from the detection of one or moreImages-Of-Interest (IOI) 1359 is presented. In some embodiments, theuser may invoke the second notification to “VIEW IMAGES”. In someembodiments, a display such as map showing the images-of-interest ofFIG. 11H is presented in response.

FIG. 11H illustrates an exemplary user interface 1170 for animages-of-interest map 1170 on a mobile device 20. Theimages-of-interest map 1170 includes a title 1172 and a display area1171 for presenting the map. Four images-of-interest are shown1480-[3,5,8,10]. These images of interested were determined from thecomputation illustrated in FIG. 13D.

Determining Locations-of-Interest

As used herein, a location-of-interest is a geographical location thatthe system predicts is of interest to a user 12 for the purpose ofidentifying images that may have been captured in proximity to thelocation-of-interest for presentation to the user.

FIG. 12A lists a table 1200 thirty-four geographic locations 1202-[1:34]used in an exemplary calculation to determine locations-of-interest. Oneof the thirty-four locations, Chapel Hill, is the home location 1202-3.As used herein, a home location is a location at which the user mostoften resides over a period of time. A home location may be specified byGPS coordinates as a point. In some embodiments, it is specified as anaddress that is converted to GPS coordinates. In some embodiments, it isauto determined by the system using location tracking history of theuser. In some embodiments, the home location is specified by a shape.The shape may be a circle, square, rectangle, or any arbitrary shape.

FIG. 12B graphically illustrates an exemplary computation 1210 fordetermining if a geographical location is a Location-of-Interest (LOI)1226. A geographical location is determined to be a Location-of-Interest(LOI) 1226 based on a score that is calculated for the geographicallocation, such as the Location Affinity Final Score (LAFS) 1224. TheLocation Affinity Final Score (LAFS) 1224 is based on a number offactors including a time (T) since a user 12 has visited thegeographical location, the distance (D) from the geographical locationrelative to a user's home location 1202-3, and the number of times (N)the user 12 has visited the location. It should be noted that a locationis deemed to have been visited when a user comes within a thresholddistance to the location. For example, a user is deemed to have visitedto Manhattan 1206-34 if they visit any part of Manhattan 1206-34. Insome embodiments, this may be implemented as coming with a thresholddistance to the location. For example, if a user comes within a mile ofa location, it may be deemed to have visited the location. Thisthreshold may be a user and/or system defined setting.

The distance (D) between two points on the earth's surface may beapproximated based on the Haversine formula shown in Equation 1 below.

$\begin{matrix}{{{Distance}(D)} = {2\; R\mspace{14mu}{{ARCSIN}\left( {{{SIN}^{2}\left( \frac{{{LAT}\; 2} - {{LAT}\; 1}}{2} \right)} + {{{COS}\left( {{LAT}\; 1} \right)}*{{COS}\left( {{LAT}\; 2} \right)}*{{SIN}^{2}\left( \frac{{{LON}\; 2} - {{LON}\; 1}}{2} \right)}}} \right)}}} & {{Equation}\mspace{14mu}(1)}\end{matrix}$

A formula for computing location affinity is shown is Equation 2. Thislocation affinity is compared to a threshold to determine if a locationis a location-of-interest.

$\begin{matrix}{{LA}_{{LOCATION}\mspace{14mu}{AFFINITY}\mspace{14mu}{THEORETICAL}} = \frac{\left( {T_{{TIME}\mspace{14mu}{SINCE}\mspace{14mu}{LAST}\mspace{14mu}{VISIT}}*D_{{DISANCE}\mspace{14mu}{FROM}\mspace{14mu}{HOME}}} \right)}{N_{{TOTAL}\mspace{14mu}{NUMBER}\mspace{14mu}{OF}\mspace{14mu}{VISITS}}}} & {{Equation}\mspace{14mu}(2)}\end{matrix}$

As can be seen from this formula, a geographical location is more likelyto be a location-of-interest if it has been a long time since the user12 has visited the location (time since last visit is a large number),the location is a long way from where the user lives (distance from homeis a large number), and/or the user 12 has never been to thegeographical location before (total number of visits is zero). In thisscenario the score will be a high number, infinite in fact, given thatthe denominator is zero.

Conversely, a neighborhood park that was visited yesterday (time sincelast visit is a small number), is close to the user's home 1242(distance from home is a small number) and has been visited frequentlyin the past (total number of visits is a large number) will produce acomparatively low score.

Modifications are made to Equation 2 to produce Equation 3. For example,the time since last visit is clipped at 10 years, the distance from theuser's home location is quantized into bands, and one is added to thenumber of visits to prevent a divide by zero error. Sample results forthis computation using Equation 3 are shown in FIG. 12A.

$\begin{matrix}{{ILAS}_{{INTERMEDIATE}\mspace{14mu}{LOCATION}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} = \frac{\left( {{CT}_{{CLIPPED}\mspace{14mu}{TIME}}*{QD}_{{QUANTIZED}\mspace{14mu}{DISTANCE}}} \right)}{\left( {\frac{{LV}_{{LOCATION}\mspace{14mu}{VISITS}}}{2} + 1} \right)}} & {{Equation}\mspace{14mu}(3)}\end{matrix}$

The results of Equation 3 are added to the Location Exclusion Bias (LEB)1221 and Location Promotion Bias (LPB) 1222. As shown in Equation 4.LAS_(LOCATION AFFINITY SCORE)=ILAS_(INTERMEDIATE LOCATION AFFINITY SCORE)+LEB_(EXCLUSION BIAS)+LPB_(PROMOTIONAL BIAS)  Equation(4)

The result of Equation 4 is used as an input to Equation 5 to introducenon-linearity to the system and to limit the result to between zeroand 1. The result of Equation 5 is then compared to a Location ThresholdValue (LTV) 1225 to determine if the location is Location-of-Interest(LOI) 1226.

$\begin{matrix}{{FLAS}_{{FINAL}\mspace{14mu}{LOCATION}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} = {{S_{SIGMOID}({LAS})} = \frac{e^{LAS}}{1 + e^{LAS}}}} & {{Equation}\mspace{14mu}(5)}\end{matrix}$

The highest scoring location in FIG. 12A is Eugene Oreg., a locationthat the user 12 has never visited and is a long distance from theuser's home 1242 (outside Chapel Hill, N.C.). Not surprisingly, thelocation that scores the lowest is the town center of Chapel Hill, whichis a short distance comparatively from the user's home and has beenvisit recently and often.

The table below shows the various inputs, intermediate results, andfinal result for computing a location affinity score.

TABLE 1 FACTORS FOR DETERMINING LOCATION AFFINITY FIELD DESCRIPTIONLocation City (LC) 1211 The city as shown in FIG. 12A. Location State(LS) 1212 The state as shown in FIG. 12A. Location Longitude GPSCoordinate The longitude GPS coordinate of the city 1211. (LLGPSC) 1213Location Latitude GPS Coordinate The latitude GPS coordinate of the city1211. (LLGPSC) 1214 Distance (D) 1215 The geographical distance in milesbetween the “home” location of Chapel Hill and the city as computedusing the haversine formula as shown in Equation 1. Quantized Distancefrom Home The quantized distance value is determined based on (QDH) 1216the quantization table shown below. The distance is the distance betweenthe user's home location and the location being tested as alocation-of-interest (the user's current location). LOWER UPPER BOUNDBOUND QD 0 <31.25 0 >=31.25 <62.5 1 >=62.5 <125 2 >=125 <250 3 >=250<500 4 >=500 <1000 5 >=1000 <2000 6 >=2000+ 7 Time (T) 1217 The time, indays, since the last time the user visited the location. Clipped Time(CT) 1218 The clipped time is equal to the time (T) clipped to a maximumvalue of 3650 days (~10 years), divided by that same maximum value of3650 days (~10 years). Location Visits (LV) 1219 Location Visits (LV)1219 is equal to the total number of life time visits to the location.More than one visit in a day is truncated to a single visit.Intermediate Location Affinity Score As calculated from Equation 3.(ILAS) 1220 Location Exclusion Bias (LEB) 1221 The exclusion bias isused to dampen or attenuate a geographical area according to an LocationExclusion Bias (LEB) 1221. The Location Exclusion Bias (LEB) 1221differs from the Location Promotion Bias (LPB) 1222 in that anadvertiser provides remuneration in exchange for application of aLocation Promotion Bias (LPB) 1222, while the Location Exclusion Bias(LEB) 1221 is applied according to user preference. The LocationExclusion Bias (LEB) 1221 is determined based on the user's currentlocation. The Location Exclusion Bias (LEB) 1221 typically has anegative value, but may have both positive and negative values in someembodiments. Location Promotion Bias (LPB) 1222 The Location PromotionBias (LPB) 1222 is applied in exchange for remuneration received from anadvertiser. The Location Promotion Bias (LPB) 1222 is determined basedon the user's current location. The Location Promotion Bias (LEB) 1222typically has a positive value, but may have both positive and negativevalues in some embodiments. Location Affinity Score (LAS) 1223 Is equalto the addition of Intermediate Score (IS) 1220, Location Exclusion Bias(LEB) 1221, and promotion bias (PW) 1222 as shown in Equation 4 LocationAffinity Final Score Is calculated from Equation 8 using the Location(LAFS) 1224 Affinity Score (LAS) 1223 as input. Location Threshold ValueThe Location Affinity Final Score (LAFS) 1224 may be (LTV) 1225 comparedto a Location Threshold Value (LT) 1225 to determine if a location is aLocation-of-Interest (LOI) 1226. Because of the nature of Equation 7, itwill always return a value between zero and 1. The Location ThresholdValue (LT) 1225 may be user designated, system designated, and/oradapted/modified over time based on a user's interactions with thesystem. In some embodiments, Location Threshold Value (LT) 1225 may beany value greater than zero and less than equal to 1. In someembodiments, values including .1, .2, .3, .4, .5, .6, .7 .8, .9 may beused. Location-of-Interest (LOI) 1226 A Boolean indicating whether thelocation is a Location-of-Interest (LOI) 1226.

Determining Images-of-Interest

As used herein, an image-of-interest is an image that the systempredicts is of interest to a user for the purpose of providingnotifications to the user identifying the location at which the imagewas captured.

Determining if an image is an image-of-interest involves firstdetermining an image affinity score and comparing it to a threshold. Theimage affinity score may be derived from zero or more subject affinityscores. The subject affinity scores are based on determining subjectsappearing in an image, determining the relationships between the userand the subject, and adding the zero or more subject affinity scorestogether to determine the image affinity score.

Determining Subject Affinity

FIG. 13A is a graphical illustration 1300 an exemplary image. The imagecorresponds to image 8 (1402-8) and contains three subject facesS2,S3,S4.

FIG. 13B is a graphical illustration 1302 of the image of FIG. 13A in anexemplary page from a social network showing activities related to animage that is used in computing subject affinity. The social networkuser account belongs to subject S1. Subject S2 has uploaded 1303 theimage 1402-8, and subjects S1-S4 have commented 1304 on the image1402-8. Subject S1 has liked 1306 the photo. Subject S2 has disliked1308 the photo.

FIG. 13C is a graphical illustration 1320 of an exemplary computationfor determining a single image affinity score. In computing an imageaffinity, a subject affinity must first be determined first. As usedherein, subject affinity refers to a strength of relationship betweenthe viewer and another subject. The subject affinity is derived from theidentity of the other subject and not the appearance or physicalattributes of the other user. As used herein, subject refers to a personwho appears in an image. As used herein, subject face refers to the faceof the subject appearing in the image. Equation 6 shown below is used indetermining subject affinity.

Table 2 describes various sources that may be used to determine subjectaffinity between a user and each of one or more subjects. The values inthe [ASW] matrix are determined according to Table 2. In the computationexample shown in FIG. 13C, the values for [ASW] are set to “1” for thesake of simplicity. As used herein, an affinity source refers to asource that may be used to infer affinity between a user and each of oneor more subjects. Those affinity sources may include Subject Face Occursin Images in User's Collection, Subject Face Occurs in Images withViewer, Subject is Symmetric Social Friend, Subject is Asymmetric SocialFriend, Subject is Contact, and Subject is Explicitly Specified.

TABLE 2 SUBJECT AFFINITY SOURCES USED IN DETERMINING SUBJECT AFFINITYAFFINITY SOURCE SYMBOL DESCRIPTION Subject Face Occurs in Images SFSubject affinity between viewer and another subject is in User'sCollection inferred by the number of times the other user appears in animage associated with the viewer. Subject Face Occurs in Images COSubject affinity between viewer and another subject is with Viewerinferred by the number of instances where the viewer and the othersubject occur in the same image. Subject is Symmetric SSF Subjectaffinity between viewer and another subject Social Friend is inferred bythe social distance between the viewer and the other subject in a socialgraph. The subject affinity between the viewer and the other subject issymmetric and the two users are “friends”. Subject is Asymmetric ASFSubject affinity between viewer and another subject is Social Friendinferred by the social distance between the viewer and the other subjectbeing followed. The subject affinity between the viewer and the othersubject is asymmetric as the viewer is “following” the other subject.Subject is Contact CL Subject affinity between viewer and anothersubject is inferred from the other subject appearing in a contact listassociated with the user Subject is Explicitly Specified EX Subjectaffinity between viewer and another subject is explicitly specified bythe viewer

Table 3 describes various subject actions that may be used to determinesubject affinity between a viewer and each of one or more subjects. Thevalues in the [AW] matrix are determined according to Table 3. In thecomputation example shown in FIG. 13C, the values for [AW] are set to“1” for the sake of simplicity. As used herein, a subject action refersto an action taken in association with an image by a subject. Thoseactions may include capturing the image, uploading or posting the image,appearing in the image, commenting on the image (including tags andannotations), and liking or disliking an image.

TABLE 3 SUBJECT ACTIONS FOR DETERMINING SUBJECT AFFINITY ACTION SYMBOLDESCRIPTION Take image T Subject took the image Upload image U Subjectuploaded and/or posted the image Appear in image A Subject appears inthe image Comment on image C Subject commented on the image. As usedherein, a comment can mean an annotation or tag. Like/Dislike LD Subjectindicated they liked or disliked an image

Subject Affinity 1343 [SA] is determined using Equation 6. Equation 6involves a matrix multiplication of four matrices. The first matrix,[ASW] 1322, is of dimension [1 1330]×[number of affinity sourcesweightings 1323]. The affinity sources weightings may be chosenempirically, and adjusted based on feedback received from the viewer(FIG. 10). The second matrix, [AO] 1324, is of dimension [affinitysources weightings 1332×affinity occurrences 1325]. The values of [AO]are determined by analyzing a viewer's data and determining values forTable 2 below. The third matrix, [SA] 1326, is of dimension [subjects1334×subject actions 1327]. The values for [SA] are determined byanalyzing subject actions in relation to a particular image. Subjectactions are described in Table 2. Finally, the fourth matrix, [AW] 1328,is of dimension [subject actions 1336×subject action weightings 1329].The subject action weightings may be chosen empirically and adjustedbased on feedback received from the viewer (FIG. 10). In the computationexample shown in FIG. 13C, the dimensions are [1×6][6×4][4×5][5×1]=[1×1]1343.

The multiplication of [ASW][AO] produces the weighted affinityoccurrences [WOA] matrix 1337. The multiplication of [SA][AW] producesthe weighted actions [WA] matrix 1339. The multiplication of [WOA][WA]produces the subject affinity score [SA] 1343.S _(AFFINITY)=[ASW][AO][SA][AW]  Equation (6)

Finally, the subject affinity result 1343 is divided by the distance ofthe viewer to the capture location of the image. Note that the distancebeing used in this computation is the distance between the currentlocation of the user as they are traveling and the capture location ofthe image, and not the home location of the user that was used inEquations 2 and 3.

$\begin{matrix}{{IIAS}_{{INTERMEDIATE}\mspace{14mu}{IMAGE}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} = \frac{{SA}_{{SUBJECT}\mspace{14mu}{AFFINITY}}}{{ID}_{{IMAGE}\mspace{14mu}{DISTANCE}}}} & {{Equation}\mspace{14mu}(7)} \\{{IAS}_{{IMAGE}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} = {{IIAS}_{{INTERMEDIATE}\mspace{14mu}{IMAGE}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} + {IEB}_{{IMAGE}\mspace{14mu}{EXCLUSION}\mspace{14mu}{BIAS}} + {IPB}_{{IMAGE}\mspace{14mu}{PROMOTIONAL}\mspace{14mu}{BIAS}}}} & {{Equation}\mspace{14mu}(8)} \\{{FIAS}_{{FINAL}\mspace{14mu}{IMAGE}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} = {{S_{SIGMOID}({IAS})} = \frac{e^{IAS}}{1 + e^{IAS}}}} & {{Equation}\mspace{14mu}(9)}\end{matrix}$

FIG. 13D is a graphical illustration 1350 an exemplary computation fordetermining images-of-interest.

TABLE 4 FACTORS FOR DETERMINING IMAGE AFFINITY FIELD DESCRIPTIONLocation 1351 The current location of the user 12 Image Latitude GPScoordinate 1352 The latitude GPS coordinate of the Location 1351 ImageLongitude GPS coordinate The longitude GPS coordinate of the Location1351 1353 Image Distance (ID) 1354 The distance between the imagecapture location and the current location of the user. Subject Affinity(SF) 1355 The image Affinity 1355 as determined from Equation 6. ImageExclusion Bias (IEB) 1356 An exclusion Bias may be applied to the ImageAffinity. The Image Exclusion Bias (IEB) 1356 is applied to thegeographic location at which the image was captured. The Image ExclusionBias (IEB) 1356 typically has a negative value, but may have bothpositive and negative values in some embodiments. Image Promotional Bias(IPB) 1357 An exclusion Bias may be applied to the Image Affinity. TheImage Exclusion Bias (IEB) 1356 is applied to the geographic location atwhich the image was captured. The Image Promotional Bias (IPB) 1355typically has a positive value, but may have both positive and negativevalues in some embodiments. Image Affinity Score (IAS) 1358 The ImageAffinity as determined in Equation 7. Final Image Affinity Score (FIAS)Is calculated from Equation 9 using the Image 1359 Affinity Score (IAS)1358 as input. Image Threshold Value (ITV) 1360 the Final Image AffinityScore (FIAS) 1359 may be compared to an Image Threshold Value (ITH) 1360to determine if an image is an Image Threshold Value (ITH) 1360. Becauseof the nature of Equation 7, it will always return a value between zeroand 1. The Image Threshold Value (ITH) 1360 may be user designated,system designated, and/or adapted/ modified over time based on a user'sinteractions with the system. In some embodiments, Image Threshold Value(ITH) 1360 may be any value greater than zero and less than equal to 1.In some embodiments, values including .1, .2, .3, .4, .5, .6, .7 .8, .9may be used. Image-Of-Interest (IOI) 1361 A Boolean indicating whetheran image is an Image-Of-Interest (IOI) 1361

In some embodiments, the Location Affinity Score (LAS) 1223 may becombined with the Image Affinity Score (IAS) 1358 to produce a compositescore before inputting to Equation 4. Some possible embodiments areshown in equations 9 and 10.

$\begin{matrix}{{CS}_{{COMPOSITE}\mspace{14mu}{SCORE}} = {{LAS}_{{LOCATION}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}}*{IAS}_{{IMAGE}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}}}} & {{Equation}\mspace{14mu}(10)} \\{{CS}_{{COMPOSITE}\mspace{14mu}{SCORE}} = {{LAS}_{{LOCATION}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}} + {IAS}_{{IMAGE}\mspace{14mu}{AFFINITY}\mspace{14mu}{SCORE}}}} & {{Equation}\mspace{14mu}(11)} \\{{FCS}_{{FINAL}\mspace{14mu}{COMPOSITE}\mspace{14mu}{SCORE}} = {{S_{SIGMOID}\left( {CS}_{{COMPOSITE}\mspace{14mu}{SCORE}} \right)} = \frac{e^{{CS}_{{COMPOSITE}\mspace{14mu}{SCORE}}}}{1 + e^{{CS}_{{COMPOSITE}\mspace{14mu}{SCORE}}}}}} & {{Equation}\mspace{14mu}(12)}\end{matrix}$

In some embodiments, the check to determine if a location is aLocation-Of-Interest may be skipped entirely.

In some embodiments, neural network techniques may be used to predictlocations-of-interest and images-of-interest. However, using neuralnetwork techniques requires sufficient training data to train the neuralnetwork model. In these embodiments, a first technique, such as thecomputations shown in Equations 1-12 may be used until such time assufficient training data has become available for training, at whichpoint, the system may switch over to using neural network predictionmodels or a hybrid approach using both techniques.

In some embodiments, the user may manually input a target geographiclocation on a map that is not the current location of the mobile device.In this embodiment, images-of-interest are determined based on thattarget geographic location without first determining if the location isa location-of-interest. In addition, the Image Distance (ID) 1354 ismodified to the distance between the image capture location and thetarget geographic location for use in Equation 7. Because a currentlocation of a device is not required, any device, including deviceslacking the hardware capabilities to determine their locations, may beused. For example, desktop personal computers.

In some embodiments, the user may manually input a target geographicarea on a map that may or may not include the current location of themobile device. In this embodiment, images-of-interest are determinedbased on that target geographic area without first determining if thearea includes a location-of-interest. In addition, the Image Distance(ID) 1354 is set to 1 for use in Equation 7. Because a current locationof a device is not required, any device, including devices lacking thehardware capabilities to determine their locations, may be used. Forexample, desktop personal computers.

FIG. 13E is a graphical illustration 1370 of the locations of imagesbeing scored overlaid on a map. Location one 1380-1, is the location ofthe viewer 12. Locations 1380-2:1380-10 are the respective locations ofnine images being evaluated. In this example, the map shows the islandof Manhattan. Images 1380-3, 1380-5, 1380-8, and 1380-10, which weredetermined to be images-of-interest in the example of FIG. 13D aremarked with a different pattern on the map.

FIG. 13F is a graphical illustration 1372 of the images being evaluated1480-2:1480-10 overlaid on the map of Manhattan 1206-34 of FIG. 13E bylocation. Again, images 1380-3, 1380-5, 1380-8, and 1380-10, which weredetermined to be images-of-interest in the example of FIG. 13D aremarked with a different pattern on the map.

FIG. 13G is a graphical illustration 1374 of a promotional bias areas1394 overlaid on the map of Manhattan 1206-34 of FIG. 13E. Fourpromotional bias areas are shown 1394-[1:4]. Note that promotional biasareas may overlap such as areas 1394-3 and 1394-4. The promotional biasareas shown in this figure do not correspond to the values 1355 used inthe example of FIG. 13D and are for illustration purposes only.

FIG. 14 is a graphical illustration 1400 of the images 1480-2:1480-8,and elements found therein, for the example shown in FIGS. 13A-13E.

FIG. 15 is a block diagram 1500 illustrating components of a machine1500, according to some embodiments, able to read instructions from amachine-readable medium 1538 (e.g., a machine-readable storage medium)and perform any one or more of the methodologies discussed herein.Specifically, FIG. 15 shows a diagrammatic representation of the machine1500 in the example form of a computer system, within which instructions1516 (e.g., software, a program, an application, an applet, an app,client, or other executable code) for causing the machine 1500 toperform any one or more of the methodologies discussed herein can beexecuted. In alternative embodiments, the machine 1500 operates as astandalone device or can be coupled (e.g., networked) to other machines1500. In a networked deployment, the machine 1500 may operate in thecapacity of a server machine or a client machine in a server-clientnetwork environment, or as a peer machine in a peer-to-peer (ordistributed) network environment. The machine 1500 can comprise, but notbe limited to, a server computer, a client computer, a personal computer(PC), a tablet computer, a laptop computer, a netbook, a set-top box(STB), a personal digital assistant (PDA), an entertainment mediasystem, a cellular telephone, a smart phone, a mobile device, a wearabledevice (e.g., a smart watch), a smart home device (e.g., a smartappliance), a digital picture frame, a TV, an Internet-of-Things (IoT)device, a camera, other smart devices, a web appliance, a networkrouter, a network switch, a network bridge, or any machine capable ofexecuting the instructions 1516, sequentially or otherwise, that specifyactions to be taken by the machine 1500. Further, while only a singlemachine 1500 is illustrated, the term “machine” shall also be taken toinclude a collection of machines 1500 that individually or jointlyexecute the instructions 1516 to perform any one or more of themethodologies discussed herein.

In various embodiments, the machine 1500 comprises processors 1510,memory 1530, and I/O components 1550, which can be configured tocommunicate with each other via a bus 1502. In an example embodiment,the processors 1510 (e.g., a central processing unit (CPU), a reducedinstruction set computing (RISC) processor, a complex instruction setcomputing (CISC) processor, a graphics processing unit (CPU), a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a radio-frequency integrated circuit (RFIC), another processor,or any suitable combination thereof) include, for example, a processor1512 and a processor 1514 that may execute the instructions 1516. Theterm “processor” is intended to include multi-core processors 1510 thatmay comprise two or more independent processors 1512, 1514 (alsoreferred to as “cores”) that can execute instructions 1516contemporaneously. Although FIG. 15 shows multiple processors 1510, themachine 1500 may include a single processor 1510 with a single core, asingle processor 1510 with multiple cores (e.g., a multi-core processor1510), multiple processors 1512, 1514 with a single core, multipleprocessors 1510, 1512 with multiples cores, or any combination thereof.

The memory 1530 comprises a main memory 1532, a static memory 1534, anda storage unit 1536 accessible to the processors 1510 via the bus 1502,according to some embodiments. The storage unit 1536 can include amachine-readable medium 1538 on which are stored the instructions 1516embodying any one or more of the methodologies or functions describedherein. The instructions 1516 can also reside, completely or at leastpartially, within the main memory 1532, within the static memory 1534,within at least one of the processors 1510 (e.g., within the processor'scache memory), or any suitable combination thereof, during executionthereof by the machine 1500. Accordingly, in various embodiments, themain memory 1532, the static memory 1534, and the processors 1510 areconsidered machine-readable media 1538.

As used herein, the term “memory” refers to a machine-readable medium1538 able to store data temporarily or permanently and may be taken toinclude, but not be limited to, random-access memory (RAM), read-onlymemory (ROM), buffer memory, flash memory, and cache memory. While themachine-readable medium 1538 is shown, in an example embodiment, to be asingle medium, the term “machine-readable medium” should be taken toinclude a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 1516. The term “machine-readable medium” shall also betaken to include any medium, or combination of multiple media, that iscapable of storing instructions (e.g., instructions 1516) for executionby a machine (e.g., machine 1500), such that the instructions 1516, whenexecuted by one or more processors of the machine 1500 (e.g., processors1510), cause the machine 1500 to perform any one or more of themethodologies described herein. Accordingly, a “machine-readable medium”refers to a single storage apparatus or device, as well as “cloud-based”storage systems or storage networks that include multiple storageapparatus or devices. The term “machine-readable medium” shallaccordingly be taken to include, but not be limited to, one or more datarepositories in the form of a solid-state memory (e.g., flash memory),an optical medium, a magnetic medium, other non-volatile memory (e.g.,erasable programmable read-only memory (EPROM)), or any suitablecombination thereof. The term “machine-readable medium” specificallyexcludes non-statutory signals per se.

The I/O components 1550 include a wide variety of components to receiveinput, provide output, produce output, transmit information, exchangeinformation, capture measurements, and so on. In general, it will beappreciated that the I/O components 1550 can include many othercomponents that are not shown in FIG. 15. Likewise, not all machineswill include all I/O components 1550 shown in this exemplary embodiment.The I/O components 1550 are grouped according to functionality merelyfor simplifying the following discussion, and the grouping is in no waylimiting. In various example embodiments, the I/O components 1550include output components 1552 and input components 1554. The outputcomponents 1552 include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor), other signal generators, and so forth. The inputcomponents 1554 include alphanumeric input components (e.g., a keyboard,a touch screen configured to receive alphanumeric input, a photo-opticalkeyboard, or other alphanumeric input components), point-based inputcomponents (e.g., a mouse, a touchpad, a trackball, a joystick, a motionsensor, or other pointing instruments), tactile input components (e.g.,a physical button, a touch screen that provides location and force oftouches or touch gestures, or other tactile input components), audioinput components (e.g., a microphone), and the like.

In some further example embodiments, the I/O components 1550 includebiometric components 1556, motion components 1558, environmentalcomponents 1560, position components 1562, among a wide array of othercomponents. For example, the biometric components 1556 includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1558 includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1560 include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometers that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensor components(e.g., machine olfaction detection sensors, gas detection sensors todetect concentrations of hazardous gases for safety or to measurepollutants in the atmosphere), or other components that may provideindications, measurements, or signals corresponding to a surroundingphysical environment. The position components 1562 include locationsensor components (e.g., a Global Positioning System (GPS) receivercomponent), altitude sensor components (e.g., altimeters or barometersthat detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication can be implemented using a wide variety of technologies.The I/O components 1550 may include communication components 1564operable to couple the machine 1500 to a network 15 or other device(s)1570 via a coupling 1582 and a coupling 1572, respectively. For example,the communication components 1564 include a network interface componentor another suitable device to interface with the network 1580. Infurther examples, communication components 1564 include wiredcommunication components, wireless communication components, cellularcommunication components, near field communication (NFC) components,BLUETOOTH® components (e.g., BLUETOOTH® Low Energy), WI-FI® components,and other communication components to provide communication via othermodalities. The devices 1570 may be another machine 1500 or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a Universal Serial Bus (USB)).

Moreover, in some embodiments, the communication components 1564 detectidentifiers or include components operable to detect identifiers. Forexample, the communication components 1564 include radio frequencyidentification (RFID) tag reader components, NFC smart tag detectioncomponents, optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as a Universal Product Code (UPC) barcode, multi-dimensional bar codes such as a Quick Response (QR) code,Aztec Code, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code,Uniform Commercial Code Reduced Space Symbology (UCC RSS)-2D bar codes,and other optical codes), acoustic detection components (e.g.,microphones to identify tagged audio signals), or any suitablecombination thereof. In addition, a variety of information can bederived via the communication components 1564, such as location viaInternet Protocol (IP) geo-location, location via WI-FI® signaltriangulation, location via detecting a BLUETOOTH® or NFC beacon signalthat may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network 1580can be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the publicswitched telephone network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a WI-FI®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1580 or a portion of the network 1580may include a wireless or cellular network, and the coupling 1580 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or another type of cellular orwireless coupling. In this example, the coupling 1582 can implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long rangeprotocols, or other data transfer technology.

In example embodiments, the instructions 1516 are transmitted orreceived over the network 1580 using a transmission medium via a networkinterface device (e.g., a network interface component included in thecommunication components 1564) and utilizing any one of a number ofwell-known transfer protocols (e.g., Hypertext Transfer Protocol(HTTP)). Similarly, in other example embodiments, the instructions 1516are transmitted or received using a transmission medium via the coupling1572 (e.g., a peer-to-peer coupling) to the devices 1570. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying the instructions 1516for execution by the machine 1500, and includes digital or analogcommunications signals or other intangible media to facilitatecommunication of such software.

Furthermore, the machine-readable medium 1538 is non-transitory (nothaving any transitory signals) in that it does not embody a propagatingsignal. However, labeling the machine-readable medium 1538“non-transitory” should not be construed to mean that the medium isincapable of movement; the medium 1538 should be considered as beingtransportable from one physical location to another. Additionally, sincethe machine-readable medium 1538 is tangible, the medium 1538 may beconsidered to be a machine-readable device.

In the embodiments described herein, the other devices 1570 may includethe mobile device 20, server device 60, and advertiser device 90. Thenetwork 1580 may include the network 15.

FIG. 16 is a block diagram 1600 illustrating an exemplary softwarearchitecture 1602, which can be employed on any one or more of themachines 1500 described above. FIG. 16 is merely a non-limiting exampleof a software architecture, and it will be appreciated that many otherarchitectures can be implemented to facilitate the functionalitydescribed herein. Other embodiments may include additional elements notshown in FIG. 16 and not all embodiments will include all of theelements of FIG. 16. In various embodiments, the software architecture1602 is implemented by hardware such as machine 1500 of FIG. 15 thatincludes processors 1510, memory 1530, and I/O components 1550. In thisexample architecture, the software architecture 1602 can beconceptualized as a stack of layers where each layer may provide aparticular functionality. For example, the software architecture 1602includes layers such as an operating system 1604, libraries 1606,frameworks 1608, and applications 1610. Operationally, the applications1610 invoke application programming interface (API) calls 1612 throughthe software stack and receive messages 1614 in response to the APIcalls 1612, consistent with some embodiments.

In various implementations, the operating system 1604 manages hardwareresources and provides common services. The operating system 1604includes, for example, a kernel 1620, services 1622, and drivers 1624.The kernel 1620 acts as an abstraction layer between the hardware andthe other software layers, consistent with some embodiments. Forexample, the kernel 1620 provides memory management, processormanagement (e.g., scheduling), component management, networking, andsecurity settings, among other functionality. The services 1622 canprovide other common services for the other software layers. The drivers1624 are responsible for controlling or interfacing with the underlyinghardware, according to some embodiments. For instance, the drivers 1624can include display drivers, camera drivers, BLUETOOTH® or BLUETOOTH®Low Energy drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), WI-FI® drivers, audiodrivers, power management drivers, and so forth. In some embodiments,the libraries 1606 provide a low-level common infrastructure utilized bythe applications 1610. The libraries 1606 can include system libraries1630 (e.g., C standard library) that can provide functions such asmemory allocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 1606 can include APIlibraries 1632 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 1606 can also include a widevariety of other libraries 1634 to provide many other APIs to theapplications 1610.

The frameworks 1608 provide a high-level common infrastructure that canbe utilized by the applications 1610, according to some embodiments. Forexample, the frameworks 1608 provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks 1608 can provide a broad spectrumof other APIs that can be utilized by the applications 1610, some ofwhich may be specific to a particular operating system 1604 or platform.

According to some embodiments, the applications 1610 are programs thatexecute functions defined in the programs. Various programming languagescan be employed to create one or more of the applications 1610,structured in a variety of manners, such as object-oriented programminglanguages (e.g., Objective-C, Java, or C++) or procedural programminglanguages (e.g., C or assembly language). In a specific example, thethird-party application 1666 (e.g., an application 1610 developed usingthe ANDROID™ or IOS™ software development kit (SDK) by an entity otherthan the vendor of the particular platform) may be mobile softwarerunning on a mobile operating system such as IOS™ ANDROID™, WINDOWS®Phone, or another mobile operating system. In this example, thethird-party application 1666 can invoke the API calls 1612 provided bythe operating system 1604 to facilitate functionality described herein.

FIG. 17 is a graphical illustration 1700 of the mobile device 20, whichis an embodiment of the hardware architecture of FIG. 15 and thesoftware architecture of FIG. 16. Most mobile devices 20, for examplehigh end cell phones, will contain most or all of the element describedin FIG. 15.

FIG. 17 is a graphical illustration 1700 of the mobile device 20, whichis an embodiment of the hardware architecture of FIG. 15 and thesoftware architecture of FIG. 16. Most mobile devices 20, moderncellular phones for example, will contain most or all the elementdescribed in FIGS. 15 & 16.

Referring now to FIG. 17, the mobile device 20 includes a controller1704 communicatively connected to memory 1706, one or morecommunications interfaces 1708, one or more user interface components1710, and one or more storage devices 1712, and location components 1714by a bus 1702 or similar mechanism. The controller 1704 may be, forexample a microprocessor, digital ASIC, FPGA, or the like. In theembodiment of FIG. 17, the controller 1704 is a microprocessor, and theclient module 23, configuration module 24, triggering module 26,notification module 28, affinity prediction module 30, UI module 32, 3Dengine module 34, AR renderer module 36, navigation module 38, imagingmodule 40, facial detection module 42, facial matching module 44, andfacial identification module 46 are implemented in software and storedin the memory 1706 for execution by the controller 1704. However, thepresent disclosure is not limited thereto. The aforementioned functionsand module may be implemented in software, hardware, or a combinationthereof. Further, the repositories 80-1 may be stored in the one or moresecondary storage components 1712. The mobile device 20 also includes acommunication interface 1708 enabling the mobile device 20 to connect tothe network 15. For example, the communications interface 1708 may be alocal wireless interface such as a wireless interface operatingaccording to one of the suite of IEEE 802.11 standards, BLUETOOTH®, orthe like. However, the present disclosure is not limited thereto. Theone or more user interface components 1710 include, for example, atouchscreen, a display, one or more user input components (e.g., akeypad), a speaker, or the like, or any combination thereof. The storagecomponent(s) 1712 is a non-volatile memory. In this embodiment, thelocation component 1714 is a hardware component, such as a GPS receiver.However, the present invention is not limited thereto.

FIG. 18 is a graphical illustration 1800 of the server device 60. Theserver device 60 is also an instance the hardware architecture of FIG.15 and the software architecture of FIG. 16, however in most embodimentsthe server device 60 will be a stripped-down implementation of themachine 1500. For example, a server device 60 may be a rack mount serverand/or a blade server and may lack many of the sensors and I/Ocomponents shown FIG. 15. Server devices 60 are often optimized forspeed, throughput, power consumption, and reliability.

Referring to FIG. 18, the server device 60 includes a controller 1804communicatively connected to memory 1806, one or more communicationsinterfaces 1808, and one or more storage devices 1812 by a bus 1802 orsimilar mechanism. The controller 1804 may be, for example amicroprocessor, digital ASIC, FPGA, or the like. In this embodiment, thecontroller 1804 is a microprocessor, and the account module 64, imagingmodule 66, map module 68, and communication module 70 are implemented insoftware and stored in the memory 1806 for execution by the controller1804. However, the present disclosure is not limited thereto. Theaforementioned module may be implemented in software, hardware, or acombination thereof. The server device 60 also includes a communicationinterface 1808 enabling the server device 20 to connect to the network15. For example, the communications interface 1808 may be a wiredinterface such as an Ethernet interface. However, the present disclosureis not limited thereto. The account repository 200, image repository230, map repository 250, communications queue repository 260, trackingdata repository 270 and promotions repository 280 may be stored in theone or more secondary storage components 1812. The secondary storagecomponents 1812 may be digital data storage components such as, forexample, one or more hard disk drives. However, the present invention isnot limited thereto.

The present disclosure is described with specificity to meet statutoryrequirements. However, the description itself is not intended to limitthe scope of this patent. Rather, the inventors have contemplated thatthe claimed subject matter might also be embodied in other ways, toinclude different steps or elements similar to the ones described inthis document, in conjunction with other present or future technologies.Moreover, although the term “step” may be used herein to connotedifferent aspects of methods employed, the term should not beinterpreted as implying any particular order among or between varioussteps herein disclosed unless and except when the order of individualsteps is explicitly described.

Throughout this specification, like reference numbers signify the sameelements throughout the description of the figures.

When elements are referred to as being “connected” or “coupled”, theelements can be directly connected or coupled together or one or moreintervening elements may also be present. In contrast, when elements arereferred to as being “directly connected” or “directly coupled,” thereare no intervening elements present.

The subject matter may be embodied as devices, systems, methods, and/orcomputer program products. Accordingly, some or all of the subjectmatter may be embodied in hardware and/or in software (includingfirmware, resident software, micro-code, state machines, gate arrays,etc.) Furthermore, the subject matter may take the form of a computerprogram product on a computer-usable or computer-readable storage mediumhaving computer-usable or computer-readable program code embodied in themedium for use by or in connection with an instruction execution system.In the context of this document, a computer-usable or computer-readablemedium may be any medium that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device.

The computer-usable or computer-readable medium may be for example, butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. By way of example, and not limitation, computer-readable mediamay comprise computer storage media and communication media.

Computer storage media is non-transitory and includes volatile andnonvolatile, removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, or other data. Computerstorage media includes, but is not limited to, RAM, ROM, EEPROM, flashmemory or other memory technology, CD-ROM, digital versatile disks (DVD)or other optical storage, magnetic cassettes, magnetic tape, magneticdisk storage or other magnetic storage components, or any other mediumwhich can be used to store the desired information and may be accessedby an instruction execution system. Note that the computer-usable orcomputer-readable medium can be paper or other suitable medium uponwhich the program is printed, as the program can be electronicallycaptured via, for instance, optical scanning of the paper or othersuitable medium, then compiled, interpreted, of otherwise processed in asuitable manner, if necessary, and then stored in a computer memory.

Communication media typically embodies computer-readable instructions,data structures, program modules or other data in a modulated datasignal such as a carrier wave or other transport mechanism and includesany information delivery media. The term “modulated data signal” can bedefined as a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal. By wayof example, and not limitation, communication media includes wired mediasuch as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media. Combinations ofany of the above-mentioned should also be included within the scope ofcomputer-readable media.

When the subject matter is embodied in the general context ofcomputer-executable instructions, the embodiment may comprise programmodules, executed by one or more systems, computers, or other devices.Generally, program modules include routines, programs, objects,components, data structures, and the like, that perform particular tasksor implement particular abstract data types. Typically, thefunctionality of the program modules may be combined or distributed asdesired in various embodiments.

Recitation of ranges of values herein are merely intended to serve as ashorthand method of referring individually to each separate valuefalling within the range, unless otherwise indicated herein, and eachseparate value is incorporated into the specification as if it wereindividually recited herein. Therefore, any given numerical range shallinclude whole and fractions of numbers within the range. For example,the range “1 to 10” shall be interpreted to specifically include wholenumbers between 1 and 10 (e.g., 1, 2, 3, . . . 9) and non-whole numbers(e.g., 1.1, 1.2, . . . 1.9).

Although process (or method) steps may be described or claimed in aparticular sequential order, such processes may be configured to work indifferent orders. In other words, any sequence or order of steps thatmay be explicitly described or claimed does not necessarily indicate arequirement that the steps be performed in that order unlessspecifically indicated. Further, some steps may be performedsimultaneously despite being described or implied as occurringnon-simultaneously (e.g., because one step is described after the otherstep) unless specifically indicated. Where a process is described in anembodiment the process may operate without any user intervention.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

The methodologies presented herein are described around the use of stillimage capture, but they are not restricted thereto. The same principlesmay be applied to the presentation of video clips captured at a locationand should be considered within the scope of the present application.

Those skilled in the art will recognize improvements and modificationsto the embodiments of the present disclosure. All such improvements andmodifications are considered within the scope of the concepts disclosedherein and the claims that follow.

What is claimed is:
 1. A server device comprising: a communicationsinterface operable to: couple the server device to a plurality of mobiledevices, the plurality of mobile devices including a first mobile deviceassociated with a first user account of a first user and a plurality ofother mobile devices associated with other user accounts of a pluralityof other users; and a processor and memory coupled to the communicationsinterface and operable to: identify a current geographic location of thefirst mobile device of the plurality of mobile devices as alocation-of-interest based on a location affinity score; identify animage captured in geographical proximity to the location-of-interest asan image-of-interest based on a first user profile of the first useraccount; and send, to the first mobile device of the plurality of mobiledevices, first information identifying the image-of-interest.
 2. Theserver device of claim 1 further comprising: the processor and thememory further operable to: identify the image captured in geographicalproximity to the location-of-interest as the image-of-interest inresponse to identifying the current geographic location of the firstmobile device of the plurality of mobile devices as thelocation-of-interest; and send, to the first mobile device of theplurality of mobile devices, the first information identifying theimage-of-interest in response to identifying the image captured ingeographical proximity to the location-of-interest as theimage-of-interest.
 3. The server device of claim 1 further comprising:the processor and the memory, in building the first user profile of thefirst user account, the first user profile including subject affinities,further operable to: retrieve a plurality of images from a first imagecollection linked to the first user account; retrieve social graphinformation for the first user account; and analyze the plurality ofimages from the first image collection and the social graph informationto determine the subject affinities between the first user of the firstmobile device and the other users of the other mobile devices.
 4. Theserver device of claim 3 further comprising: the processor and thememory, in determining the subject affinities of the first user profile,further operable to: analyze symmetric social network connectionsbetween the first user and ones of other users; analyze asymmetricsocial network connections between the first user and the ones of otherusers; analyze occurrences of subject faces of the plurality of otherusers appearing in the plurality of images in the first imagecollection; and analyze co-occurrences of a subject face of the firstuser of the first mobile device and other subject faces of the pluralityof other users of the other mobile devices occurring the plurality ofimages in the first image collection.
 5. The server device of claim 1further comprising: the processor and the memory in building the firstuser profile of the first user account, the first user profile includinga geographical location history, further operable to: collect geographiclocation data for the first mobile device on a periodic basis; store thegeographic location data in a tracking database; and determine a homelocation using the geographic location data.
 6. The server device ofclaim 1 further comprising: the processor and the memory, in determiningthe location affinity score, further operable to: determine a distancebetween the current geographical location and a home geographicallocation; determine a number of days since the current geographicallocation was visited; determine a total number of times that the currentgeographical location was visited; and determine the location affinityscore based on the distance between the current geographical locationand the home geographical location, the number of days since the currentgeographical location was visited, and the total number of times thatthe current geographical location was visited.
 7. The server device ofclaim 6 further comprising: the processor and the memory, in determiningif the current geographical location of a first device represents thelocation-of-interest, further operable to: compute the location affinityscore to be proportional to the distance between the currentgeographical location and the home geographical location a last timethat the current geographical location was visited and inverselyproportional to the total number of times that the current geographicallocation was visited.
 8. The server device of claim 6 furthercomprising: the processor and the memory further operable to: modify thelocation affinity score based on an exclusion bias applied to thecurrent geographic location, wherein the exclusion bias is determinedfrom taking a sum of one or more geographic shapes in which the currentgeographic location falls, each shape derived from a user profile of auser; and modify the location affinity score based on a promotional biasapplied to the current geographic location, wherein the promotional biasis determined from taking the sum of one or more geographic shapes inwhich the current geographic location falls, each of the one or moregeographic shapes specified by an advertiser and assigned a value basedon a remuneration received from the advertiser.
 9. The server device ofclaim 6 further comprising: the processor and the memory furtheroperable to: perform a comparison of the location affinity score to alocation threshold value; and based on the comparison, determine thatthe image is the image-of-interest.
 10. The server device of claim 1further comprising: the processor and the memory in identifying one ormore images-of-interest in the geographical proximity to thelocation-of-interest further operable to: designate a search area basedon the location-of-interest; and identify the image as having beencaptured within the search area surrounding the location-of-interest asthe image-of-interest.
 11. The server device of claim 10 wherein a sizeof the search area is designated based on a speed of travel of the firstmobile device as measured over a time interval.
 12. The server device ofclaim 10 wherein a shape of the search area is designated based on adirection of travel of the first mobile device as measured over a timeinterval.
 13. The server device of claim 10 wherein a size of the searcharea is designated based on an altitude of travel of the first mobiledevice as measured over a time interval.
 14. The server device of claim1 further comprising: the processor and the memory, in determining ifthe image represents the image-of-interest, further operable to:determine an image affinity score; and based on the image affinityscore, determine if the image is the image-of-interest.
 15. The serverdevice of claim 14 further comprising: the processor and the memory, indetermining the image affinity score, further operable to: determinesubjects associated with the image by identifying: a first other userhaving captured the image; one or more second other users appearing assubject faces in the image; and one or more third other userscontributing comments to the image; and determine action weights by:designating a first action weight for the one or more second other usersappearing as subject faces in the image; designating a second actionweight for the one or more third other users contributing comments tothe image; and designating a third action weight for the one or morethird other users contributing comments to the image; and compute theimage affinity score by: determining a first partial score based on afirst subject affinity of the first other user and the first actionweight; determining a second partial score based on second subjectaffinities of the one or more second other users and the second actionweight; and determining a third partial score based on third subjectaffinities of the one or more third other users and the third actionweight; and determining the image affinity score based on the firstpartial score, the second partial score, and the third partial score.16. The server device of claim 15 further comprising: the processor andthe memory further operable to: scale the subject affinities of the oneor more second other users appearing as the subject faces in the imagebased on a size of a subject face in relation to dimensions of the imageand a location of the subject face within the image.
 17. The serverdevice of claim 15 further comprising: the processor and the memory, indetermining the image affinity score, further operable to: determine adistance between the current geographical location and a capturelocation of the image; and modify the image affinity score based on thedistance.
 18. The server device of claim 15 further comprising: theprocessor and the memory, in determining the image affinity score,further operable to: modify the image affinity score based on anexclusion bias applied to a geographic capture location of the image,wherein the exclusion bias is determined from taking a sum of one ormore geographic shapes in which the geographic capture location of theimage falls, each of the one or more shapes derived from a user profileof a user; and modify the image affinity score based on a promotionalbias applied to the geographic capture location of the image, whereinthe promotional bias is determined from taking the sum of one or moregeographic shapes in which the geographic capture location of the imagefalls, each of the one or more geographic shapes specified by anadvertiser and assigned a value based on a remuneration received fromthe advertiser.
 19. The server device of claim 15 further comprising:the processor and the memory, in determining the image affinity score,further operable to: compute the image affinity score for the image tobe: proportional to the subject affinity score; and inverselyproportional to a distance between the current geographical location anda geographic capture location.
 20. The server device of claim 15 furthercomprising: the processor and the memory, in determining if the imagerepresents the image-of-interest, further operable to: perform acomparison of the image affinity score to an image threshold value; andbased on the comparison, determine that the image is theimage-of-interest.
 21. The server device of claim 1 further comprising:the processor and the memory, in sending the first informationidentifying the image-of-interest, further operable to: receive, fromthe first mobile device of the plurality of mobile devices, secondinformation identifying user interactions with the image-of-interest atthe first mobile device; and adjust parameter weights of subjectaffinity sources and subject actions based on the second informationidentifying the user interactions.
 22. The server device of claim 21further comprising: the processor and the memory, in sending the firstinformation identifying the image-of-interest, further operable to: sendpromotional information associated with the image to the first mobiledevice, the promotional information including one or more adornmentsconfigured for presentation with the image.
 23. The server device ofclaim 21 further comprising: the processor and the memory, in sendingthe first information identifying the image-of-interest, furtheroperable to: receive, from the first mobile device, user interactionfeedback, the user interaction feedback including: time differencebetween a user device of a user receiving a notification identifying theimage, time spent by the user device of the user interacting with theimage, comments added using the user device of the user, likes appliedusing the user device of the user, and dislikes applied using the userdevice of the user.
 24. The server device of claim 23 furthercomprising: the processor and the memory further operable to: receive,from the plurality of other mobile devices associated with the otheruser accounts of the plurality of other users, a plurality of otherimages; and identify, from the plurality of other images, theimage-of-interest.
 25. The server device of claim 1 wherein theimage-of-interest is one of one or more images-of-interest and thelocation-of-interest is one of one or more locations-of-interest.
 26. Amethod of operating a server device comprising: identifying a currentgeographic location of a mobile device as a location-of-interest basedon a location affinity score; identifying an image captured ingeographical proximity to the location-of-interest as animage-of-interest based on a user profile of a user account associatedwith the mobile device; and sending, to the mobile device, informationidentifying the image-of-interest.
 27. A non-transient computer readablemedium storing program codes that when executed by a processor of aserver device render the server device operable to: identify a currentgeographic location of a mobile device as a location-of-interest basedon a location affinity score; identify an image captured in geographicalproximity to the location-of-interest as an image-of-interest based on auser profile of a user account associated with the mobile device; andsend, to the mobile device, information identifying theimage-of-interest.